You paste the error into your AI tool. It thinks for a moment, rewrites some code, and tells you with total confidence: "Fixed! The issue was X, and I've resolved it." You reload the app, the thing that was broken seems to work, and you move on. Then three days later the same bug is back, or a user reports something new that used to work fine.

I see this almost every week. A non-technical founder needs to verify an AI fixed a bug, but the only evidence they have is the AI's own say-so. That is a problem, because AI tools are cheerful about declaring victory whether or not they actually won. Taking that word at face value is how bugs come back to haunt you at the worst possible time.

So let me give you a way out. In this post I'll walk you through a concrete, non-technical framework for proving a fix actually worked, that it didn't just hide the symptom, and that it didn't quietly break something else while it was in there.

The problem: "fixed" is not the same as fixed

When an AI says it fixed your bug, it is making a prediction, not a measurement. It is telling you what it believes its code change will do, based on patterns it has seen. It did not run your app. It did not click through your signup flow. It did not check whether the payment actually cleared. It changed some text and moved on.

That gap matters enormously. There are three very different outcomes hiding behind the word "fixed":

  • It's actually fixed. The root cause is gone, and it stays gone.
  • It's papered over. The visible symptom disappears, but the underlying cause is still there, waiting to resurface under slightly different conditions.
  • It's traded for a new bug. The original problem is gone, but the change broke something else you haven't noticed yet.

Without a way to tell these apart, you are flying blind. You ship the "fix," breathe a sigh of relief, and find out weeks later which of the three you actually got. That uncertainty is the real enemy here, and it is completely fixable.

Why AI tools declare victory too early

This is not the AI being dishonest. It is a direct result of how these tools work, and once you understand it, you stop trusting the confident tone.

An AI coding tool optimizes for producing a plausible answer to the prompt in front of it. When you paste an error, its job (as it understands it) is to generate code that would make that specific error go away. It is very good at that narrow task. It is not thinking about your whole app, your real users, or the ten other places that piece of code touches.

A few specific reasons the "it's fixed" claim runs ahead of reality:

  • It can't see the result. The AI does not run your app against real data. It has no feedback loop telling it whether the change worked, so "fixed" is a guess dressed up as a fact.
  • It treats the symptom as the disease. If the error was "payment failed," it will happily make the error message stop appearing without ensuring the payment actually goes through. Symptom gone, problem intact.
  • It has tunnel vision. It focuses on the lines you pointed at and rarely considers what else depends on them. This is exactly why fixing one thing so often breaks another thing in AI-built apps.
  • It repeats its own blind spots. Asking the same AI that wrote the bug to verify its own fix is like asking a student to grade their own exam. It re-checks against the same flawed assumptions.

The AI can tell you what it changed. It cannot tell you whether your app works. Only testing the real thing can do that.

That is why the burden of proof falls on you. The good news: you do not need to read code to carry it.

The framework: how to verify an AI fixed a bug

Here is the routine I use, translated into steps a non-technical founder can run without touching the code. Think of it as three questions you answer in order: Can I make it break? Is it gone? Did anything else move?

Step 1: Write down the bug before you fix it

You cannot prove a bug is gone if you were fuzzy about what it was in the first place. Before you ask the AI to fix anything, write down two things in plain English:

  1. The exact steps to trigger it. For example: "Log in as a new user, add an item to the cart, click checkout, enter a valid card. The page spins forever and no charge appears."
  2. What should happen instead. "The charge goes through, I land on a confirmation page, and the order shows in my dashboard."

This is your test case. It is the single most valuable thing you can create, and almost nobody does it. Write it in a note somewhere you'll keep it, because you'll reuse it every time this area of the app changes.

Step 2: Reproduce the bug yourself first

Before the fix, run your own steps and confirm you can actually make the bug happen. This sounds obvious, but it catches a surprising trap: sometimes the bug is intermittent, or depends on specific data, and if you can't reliably trigger it, you can't reliably prove it's gone either.

If you can make it break on demand, you now have a real, repeatable test. If you can't reproduce it at all, that's important information too. It usually means the cause is something environmental (a slow third-party service, a data state, a timing issue) and no code change should be trusted until you understand it.

Step 3: After the fix, run your exact steps again

Now apply the AI's fix and run your written steps word for word. Not "something like" your steps. The exact same ones. Watch what happens.

If the bug is gone, good, but you are not done. You've only proven the symptom disappeared. Next you have to make sure it's actually gone and not just hiding.

Step 4: Try to make it break again, on purpose

This is the step that separates a real fix from a paper-over. Poke at the edges of the same feature:

  • Run the steps three or four times in a row. Some bugs only show up on the second attempt or when data already exists.
  • Try the messy version. Use a weird email, a huge quantity, a slow connection, the back button, a double-click on the submit button. Real users do all of these.
  • Try it as a different user. Log in as someone else, or as a brand-new account. Bugs love to hide behind "well, it worked for my account."

If the bug survives none of this, you have real evidence the fix holds. If it comes back under any of these, the AI treated the symptom, not the cause. This kind of hands-on poking is the heart of proper QA for AI-built apps, and it's where most silent bugs get caught.

Step 5: Check the neighbors

A fix in one place can dent something nearby. This is the "traded for a new bug" outcome, and it's the one founders miss most, because they only test the thing they just fixed.

Make a short list of the features that touch the same area and run through them:

  • If the fix was in checkout, test login, the cart, and your order history.
  • If the fix was in login, test signup, password reset, and logging out.
  • If the fix was in anything that saves data, reload the page and confirm the data is really there, not just showing on screen.

You don't need to test the whole app. You need to test the neighbors. Ten minutes here saves you a nasty surprise later.

Step 6: Ask the AI to explain the root cause in plain English

Now turn the AI's confidence into something useful. Instead of accepting "Fixed!", ask it a pointed question:

In plain English, what was the actual root cause of this bug,
and how does your change prevent it from happening again?
Don't just tell me what you changed. Tell me why it broke.

If the answer names a real cause ("the code assumed the user always had a saved address, and crashed when they didn't"), that's a good sign. If the answer is vague ("I improved the error handling and made the code more robust"), be suspicious. Vague explanations often mean it hid the symptom and doesn't actually understand what went wrong. When the AI genuinely can't explain the cause, you may be stuck in a loop the AI can't fix, and that's a signal to get a human involved.

The signals that a "fix" is fake

Over the years I've learned to spot a paper-over fix quickly. Watch for these:

  • The error message is gone but the outcome is still wrong. No error, but the payment still didn't land or the email still didn't send. Classic symptom suppression.
  • The AI wrapped everything in a "try again" or hid the error. Silencing an error is not fixing it. It just means the failure now happens quietly, which is worse.
  • The same bug comes back under slightly different conditions. A real fix addresses the cause. If a small change in input brings it right back, the cause is still there.
  • A new, unrelated thing broke right after. Strong sign the change had side effects nobody checked.
  • The AI's explanation keeps changing. If you ask twice and get two different stories about what was wrong, it's guessing.

If you're seeing several of these, don't keep pasting the error back in for another round. That cycle tends to pile up invisible debt as each patch layers on top of the last one.

What you can do yourself, and where a human helps

Most of this framework is genuinely yours to run. Writing the test case, reproducing the bug, testing the neighbors, and pushing on the edges are all things you can do without reading a single line of code. Doing just that will catch the majority of fake fixes before they reach your users.

There's an honest limit, though. Some bugs live in places you can't see from the outside: whether data is actually saved correctly under the hood, whether a fix left a security hole open, whether the change quietly made your app slower or more expensive to run. A symptom can vanish from the screen while the real problem sits untouched in the database or in a permission check. That's true for a lot of auth and login bugs especially, where "it logs me in fine" can coexist with a serious hole.

That's the part I get called in for. When you've run the framework and something still feels off, or the same bug keeps coming back no matter how many times the AI swears it's fixed, that's usually a sign the cause is deeper than the surface. I look at where the AI actually made the change, confirm whether it addressed the root cause or just the symptom, and check the things you can't see from the front end.

You don't have to become a developer to stop getting fooled by "Fixed!" You just need a repeatable way to demand proof, and a real person to call when the proof doesn't add up. If a bug in your AI-built app keeps coming back and you're tired of taking the AI's word for it, let's take a look together and find out what's really going on.

Cover photo by Daniil Komov on Pexels.